A new approach to experimental design for function approximation and classification: the twilight method

Modeling real-world systems plays an essential role in system analysis, and contributes to abetter understanding of their behavior and performance. classification, optimization, controls, and patternrecognition problems heavily rely on modeling techniques. from a particular viewpoint, models could becategorized into three classes: white box, black box, and gray box models. the present study focuses onblack box modeling. the satisfactory performance of a black box model depends on its structure and dataused for calibration of the model. although the number of data points is an important factor for improvingthe richness of the dataset, there are limitations on increasing the number of data points in real problems.for instance, gathering data from many real-life systems (e.g. industrial ones) imposes spending a hugeamount of time and money. in this study, we discuss a method which yields richer datasets for a knownnumber of data, in comparison to some other conventional experimental design methods. in the proposedalgorithm, after extracting some data points by the factorial design method, the remaining data points areextracted based on the analysis of the available data and the characteristics of the model. the resultsillustrate the superior efficiency of the proposed method